There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive $M$-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-light channels' secrecy capacity and bit error rate (BER) performances. The proposed design is motivated by higher-order modulation, which results in better secrecy capacity at the expense of a higher BER. On the other hand, a proper precoding design, which can manipulate the received signal quality at the legitimate user and the eavesdropper, can also enhance secrecy performance and influence the BER. A reward function that considers the secrecy capacity and the BERs of the legitimate user's (Bob) and the eavesdropper's (Eve) channels is introduced and maximized. Due to the non-linearity and complexity of the reward function, it is challenging to solve the optical design using classical optimization techniques. Therefore, reinforcement learning-based designs using Q-learning and Deep Q-learning are proposed to maximize the reward function. Simulation results verify that compared with the baseline designs, the proposed joint designs achieve better reward values while maintaining the BER of Bob's channel (Eve's channel) well below (above) the pre-FEC (forward error correction) BER threshold.

翻译：物理层安全（PLS）相比传统密码学能提供独特的信息保密性保障方法，近年来受到越来越多的关注。本文研究自适应$M$进制脉冲幅度调制（PAM）与预编码的联合设计，旨在优化窃听可见光信道的保密容量和误码率（BER）性能。该设计的动机源于高阶调制虽能提升保密容量，但会导致BER增高；而适当的预编码设计可操控合法用户与窃听者的接收信号质量，同时增强保密性能并影响BER。本文引入并最大化一个兼顾保密容量与合法用户（Bob）和窃听者（Eve）信道BER的奖励函数。由于该奖励函数的非线性和复杂性，经典优化方法难以求解该光学设计问题，因此提出基于Q学习和深度Q学习的强化学习设计方案来最大化该奖励函数。仿真结果表明，与基线方案相比，所提出的联合设计在将Bob信道（Eve信道）BER维持在远低于（高于）前向纠错（FEC）BER阈值的同时，实现了更优的奖励值。